Imaging modality maintenance smart dispatch systems and methods

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a technician selector to: identify at least one of a skill level, tools list, or replacement part list to fix a problem based on an identified problem corresponding to a service request from an imaging device; and access resources from a database of resources available to service the imaging device; a multiplier to weight resources based on at least one of the skill level, possessed tools in comparison to the tools list, possessed replacement parts in comparison to the replacement part list, distance to service location, or availability; and an interface to transmit the service request using a wireless communication to a repair device of the highest weighed resource, the service request to be augmented to include a configuration for the repair device to facilitate addressing of the service request by the highest weighted resource.

FIELD OF THE DISCLOSURE

This disclosure relates generally to imaging systems, and, moreparticularly, to image modality maintenance smart dispatch systems andmethods.

BACKGROUND

Manufactures of large machines (e.g., imaging machines in health care,turbines in energy, and engines in transportation) deploys such largemachines to users/customers for use in the field. Due to thecomplications of such machines, some manufactures provide repair and/orupkeep services with teams of technicians to service the machines duringscheduled maintenance and/or when the machine is malfunctioning and/ordown. When a user has a problem with a deployed machine, the usercontacts the manufacturer (e.g., via call, email, etc.) describing theproblem (e.g., providing symptoms) and a technician is sent to fix themachine. Additionally, the manufacture and/or customer can schedulemaintenance calls at set durations of time to verify that the machine isworking properly.

SUMMARY

Certain examples provide a computer readable medium comprisinginstructions which, when executed, cause a machine to provide an imagemodality maintenance smart dispatch. The example computer readablemedium includes instructions that cause the machine to model a problemusing an artificial intelligence model of an imaging device based oninformation obtained from a service request from the imaging device. Theexample computer readable medium further includes instructions thatcause the machine to identify at least one of a skill level, tools list,or replacement part list to fix the problem based on the modeling usingthe artificial intelligence model. The example computer readable mediumfurther includes instructions that cause the machine to access resourcesfrom a database of resources available to service the imaging device toidentify a set of one or more available resources. The example computerreadable medium further includes instructions that cause the machine toin response to the service request for the imaging device correspondingto an issue, determine whether the issue of the imaging devicecorresponds to a critical error. The example computer readable mediumfurther includes instructions that cause the machine to when the issueof the imaging device corresponds to the critical error, filter outresources that are not available to service the imaging device within athreshold amount of time to reduce the set of one or more availableresources. The example computer readable medium further includesinstructions that cause the machine to weight resources in the set ofone or more available resources based on at least one of the skilllevel, possessed tools in comparison to the tools list, possessedreplacement parts in comparison to the replacement part list, distanceto service location, or availability to identify a highest weightedresource. The example computer readable medium further includesinstructions that cause the machine to transmit the service requestusing a wireless communication to a repair device associated with thehighest weighed resource.

Certain examples provide an apparatus to provide an image modalitymaintenance smart dispatch. The example apparatus includes a technicianselector to identify at least one of a skill level, tools list, orreplacement part list to fix a problem based on an identified problemcorresponding to a service request from an imaging device and accessresources from a database of resources available to service the imagingdevice. The apparatus further includes a multiplier to weight resourcesbased on at least one of the skill level, possessed tools in comparisonto the tools list, possessed replacement parts in comparison to thereplacement part list, distance to service location, or availability.The apparatus further includes an interface to transmit the servicerequest using a wireless communication to a repair device of the highestweighed resource, the service request to be augmented to include aconfiguration for the repair device to facilitate addressing of theservice request by the highest weighted resource

Certain examples provide a method to provide an image modalitymaintenance smart dispatch. The example method includes identifying atleast one of a skill level, tools list, or replacement part list to fixa problem of a machine based on a digital twin of the machine. Themethod further includes accessing resources from a database of resourcesavailable to service an imaging device. The method further includesdetermining whether the problem of the imaging device corresponds to acritical error. The method further includes when the problem of theimaging device does correspond to the critical error, removing resourceswho are not available to service the imaging device within a thresholdamount of time. The method further includes weighting resources based onat least one of the skill level, possessed tools in comparison to thetools list, possessed replacement parts in comparison to the replacementpart list, distance to service location, or availability to determine ahighest weighted resource. The method further includes transmitting aservice request corresponding to the imaging device to a repair deviceof the highest weighed resource using a wireless communication.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and/or 1B illustrate an example environment including a machinerecovery system for servicing a universe of machines.

FIG. 2 is a block diagram of an example implementation of the machinerecovery system of FIGS. 1A and/or 1B.

FIG. 3 is a representation of an example digital twin.

FIGS. 4A-4C depict example devices to assist in diagnosis and repair ofequipment.

FIG. 5 is a representation of an example deep learning neural networkmodel.

FIG. 6 is a flowchart representative of machine readable instructionswhich can be executed to implement the machine recovery system of FIGS.1A and/or 1B to handle a service request from a machine of FIGS. 1Aand/or 1B.

FIG. 7 is a flowchart representative of machine readable instructionswhich can be executed to implement the machine recovery system of FIGS.1A and/or 1B to perform a smart symptom process.

FIG. 8 is a flowchart representative of machine readable instructionswhich can be executed to implement the machine recovery system of FIGS.1A and/or 1B to perform a smart care package process.

FIG. 9 is a flowchart representative of machine readable instructionswhich can be executed to implement the machine recovery system of FIGS.1A and/or 1B to perform a smart dispatch.

FIG. 10 is a flowchart representative of machine readable instructionswhich can be executed to implement the machine recovery system of FIGS.1A and/or 1B to perform a smart find.

FIG. 11 is a block diagram of an example processing platform structuredto execute the instructions of FIGS. 6-10 to implement the machinerecovery system of FIGS. 1A and/or 1B.

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Fleet of machines, such as, but not limited to, imaging systems,turbines and engines are increasingly being deployed over largegeographic regions. In the medical field, imaging systems includingmodalities such as magnetic resonance imaging (MRI), computed tomography(CT), nuclear imaging, and ultrasound are increasingly being deployed inhospitals, clinics, and medical research institutions for medicalimaging of subjects. Engines deployed in locomotives or aircrafts, needto operate under varying environmental conditions. In power generationsystems, wind turbines or water turbines are installed to harvest energyfrom natural resources. For facilities owning a machine belonging to afleet of machines, it is desirable to maximize utilization of themachine with minimal downtime. However, system failures and breakdownsinterrupt the workflow processes involving the machine and reduce itsutilization.

Most manufacturers strive to provide effective periodic maintenanceroutines and responsive or on call repair services. Despite the refinedcapability of preventive maintenance programs, machines can sometimesdevelop problems which need out of turn diagnosis and repair. Usually,such problems are identified by a concerned authority at the facilitiesthat manage the installed machine. The identified problems are submittedas service requests in one or more formats, such as, but not limited to,a textual description through a webform and a voice call through ahelpline. As used herein, the term “service request” refers to adescription of a problem, a fault, or issue associated with a machine,such as an imaging system. The problem, fault, or issue can be observedduring routine maintenance check, or during usage of the machine, forexample, by a technician or a user. The service request can be adescription in text or audio message provided by the user via a userinterface and can be automatically stored in a database.

Traditionally, servicing of a machine among the fleet of machines suchas the imaging systems can require parts replacement or on-site visitsby a field engineer to the site. Such on-site visits by field engineerscan be expensive and time consuming for both customer and systemmanufacturer or repair facility, who typically arranges for such visits.Remote diagnosis and repair are often used to expedite system repair andobviate or minimize the need for such on-site visits. However, existingremote diagnosis and repair still entails the need to interrupt use ofthe imaging system and contact with the repair facility. Also, uponidentification of a fault using remote diagnosis, manual interventioncan be needed to submit a service request, initiate service requestprocessing, and identify the requirement of an on-field visit.Traditionally, an expert is required to manually scan huge amount ofdata pertaining to service requests, to make and/or recommend decisionsabout servicing options based on the service requests. Manual processingof service requests is inefficient and adversely effects the responsetime. Reducing manual overhead while processing servicing requestswithout compromising on accuracy and response times is desirable.

Examples disclosed herein provide a system for maintenance of machinesthat solves the problems of traditional techniques. Examples disclosedherein efficiently process service requests of machines belonging to afleet of machines such as healthcare imaging systems, without or withminimal inherent work-flow delays. Examples disclosed herein include (A)a system to convert customer defined symptoms of a machine to actualproblem/issues of the machine (e.g., smart symptom), (B) a system todevelop a care package to provide to a resource (e.g., a technician, anautomated program, etc.) that includes targeted and relevant informationthat can be useful in servicing the machine (e.g., smart care package),(C) a system to determine which technicians are best suited to servicethe request based on the customer demands (e.g., smart dispatch), (D) asystem to identify a fleet of machine that correspond to the identifiedmachine to use and/or develop an artificial intelligence model (e.g., adeep or machine learning model/digital twin) corresponding to themachine (e.g., smart find), and (E) a system to use technician feedbackregarding the service request to update and fine tune the smart symptom,smart care package, smart dispatch, and smart find processes.

FIG. 1A is a schematic illustration of an environment 100 for servicingmachines from a universe of machines. The environment 100 includes amachine recovery system 102, a machine 104, a service center 106, and atechnician device 108. Although the environment 100 includes one machine104, the environment is described in conjunction with a plurality ofmachines located in the same and/or difference locations.

The machine recovery system 102 of FIG. 1A performs various processes inresponse to a service request for the machine 104 and builds knowledgebased on previous service request. Initially, the machine recoverysystem 102 receives a service request from the service center 106 and/ordirectly from the machine 104 when a problem occurs. For example, a userof the machine 104 can call and/or the machine 104 can otherwisecommunicate with the service center 106 to identify a problem with themachine and/or describe symptoms of the machine 104. In another example,the user can interface with a user interface of the machine 104 or auser interface of a computer to request servicing and/or identifysymptoms of the machine 104. The machine recovery system 102 may, basedon the request, perform a smart symptom process, a smart care packageprocess, a smart dispatch process, a smart find process, and/or updatedata based on the servicing of requests from the machine 104 and othermachines in a universe. The smart symptom process converts identifiedsymptoms into machine issues. For example, a user of the machine 104 maynot be able to determine the actual issue that the machine 104 isexperiencing, but the user can identify symptoms that can be convertedinto one or more issues. The smart care package provides customized,relevant, actionable information to the technician device 108. Thecustomized, relevant, actionable information corresponds to relevantmanual/tutorial information, machine information, necessary tools,replacement parts, artificial intelligence model (e.g., deep or machinelearning network model(s)/digital twin, etc.) information, predictedproblems and/or solutions for the servicing, etc. The machine recoverysystem 102 generates the smart care package based on databaseinformation corresponding to previous servicing of the machine 104and/or other machines. The smart dispatch process determines whichresource(s) (e.g., technician(s), automated repair programs, etc.) toselect to service the request based on various factors including, forexample, the distance to the location, skill level of the technicians,availability of the technician, etc. The smart find system identifies afleet of machines corresponding to the particular machine 104 based onthe model, modality, make, and/or of contextual information of themachine 104. The fleet corresponds to a group of similar machines thatcan experience the same issues. Accordingly, the fleet corresponds to anartificial intelligence model/digital twin that can be used to testsolutions virtually, during/prior to servicing. Additionally,information of the service request of the machine 104 can be added tothe model/digital twin of the machine to update the model/digital twinfor subsequent service requests from other machines in the fleet.Additionally, the machine recovery system 102 updates information invarious databases (e.g., enterprise systems, problem solution database(PSDB), machine log database(s), and/or dynamic system health databases)to refine the smart symptom, smart care package, smart dispatch, andsmart find processes for subsequent service requests of the machine 104and other machines in the universe. The machine recovery system 102 isfurther described below in conjunction with FIG. 2.

The machine 104 of FIG. 1A is an imaging device or system that canrequire periodic servicing (e.g., based on a schedule) or aperiodicservicing (e.g., based on presence of an issue). Alternatively, themachine 104 can be any type of machine that can require periodicservicing or aperiodic servicing. For example, the machine 104 can be animaging system (e.g., magnetic resonance imaging (MRI), computedtomography (CT), x-ray, nuclear imaging, or ultrasound, etc.), a turbine(e.g., wind turbine, water turbine, etc.), an engine (e.g., locomotiveengine, aircraft engine, etc.), an information system (e.g., an imagingworkstation, a picture archiving and communication system, a radiologyinformation system, an image archive, an electronic medical recordsystem, etc.), etc. When the machine 104 experiences an issue, a usercan transmit a service request via a computer, via a phone (e.g.,calling or texting), and/or the machine 104 itself can transmit aservice request when an error or unexpected result occurs. The servicerequest can be sent to the service center 106 and/or directly to themachine recovery system 102. In some examples, keywords from the servicerequest can be extracted and processed to identify symptoms and/orissues with the machine 104. In some examples, the machine recoverysystem 102 can transmit distinguishing symptoms (e.g., directly or viathe service center 106) to the user of the machine 104 to filter outpossible issues.

The technician device 108 of FIG. 1A is a computing device (e.g., alaptop, a smart phone, a tablet, headset, etc.) that the technicianand/or other resource can use to manage a schedule, receive servicerequests, look up information related to one or more machines to beserviced, identify error codes, symptoms, issues, and/or solutions,model the machine 104 (e.g., using an artificial intelligence model) toassist with repair, and/or interface with the imaging device to obtainadditional data and/or confirm data. An implementation of exampleartificial intelligence models are further described below inconjunction with FIGS. 3 and/or 5. The machine recovery system 102provides a custom care package including the relevant information thatcan be helpful for the technician or other resource to service therequest. The care package can include one or more diagnostic and/orrepair applications, knowledge base information, tool(s), script(s),routine(s), workflow(s), database(s), artificial intelligence model(s)(e.g., digital twin(s)) etc., to diagnose, remedy, and/or aid a resource(e.g., a technician, an automated service repair program, etc.) and/orthe machine 104 in addressing an issue, for example.

When the technician device 108 of FIG. 1 has access to a digital twin,the technician device 108 (e.g., automatically) and/or a user of thetechnician device 108 can implement different service techniques (e.g.,perform virtual actions, execute virtual instructions, etc.) on thedigital twin to identify a potential problems that that can occur duringthe repair and/or identify an unexpected error that did occur during therepair. Additionally, after a resource fixes the machine 104, theresource can indicate that the service request is complete via thetechnician device 108. After such an indication, the machine recoverysystem 102 transmits a survey to the technician device 108 to obtaininformation corresponding to the service request and/or the carepackage. Additionally or alternatively, the technician device 108 canautomatically output a survey to a technician once the service iscomplete and transmit the results to the machine recovery system 102. Insome examples, the technician device 108 can interface (e.g., directlyor via a wireless communication) with the machine 104 to gather dataprior to, during, and/or after the machine 104 has been serviced. Suchdata can be transmitted to the machine recovery system 102 (e.g.,separately and/or as part of a feedback response). The survey results(e.g., feedback) and/or obtained data can be used by an artificialintelligence model of the machine recovery system 102 to optimize futurecare packages, dispatches, machine finds, diagnoses (e.g., smart symptomprocesses), etc., to update databases to provide better recommendationsfor a subsequent service request and/or identify future problems forsimilar machines. Implementations of example technician devices 108 arefurther described below, in conjunction with FIGS. 4A-4C.

In some examples, the technician device 108 can be implemented as arepair device (e.g., for device-to-device repairs and/or to facilitatetechnician repair). For example, the technician device 108 can interfacewith the machine 104 (e.g., via a wired or wireless connection) totransmit instructions to the machine 104 to run diagnosis, gatherinformation, and/or repair the device 108. For example, the techniciandevice 108 can be a server or other network connected device that pushesinstructions to the machine 104 (e.g., for remote repair). Theinstructions can cause the machine 104 to load a new version of code,apply a code patch, reboot the machine, and/or perform other processesto repair the machine 108.

FIG. 1B illustrates information and/or process that may be performed inthe example environment 100 of FIG. 1. FIG. 1B includes the machinerecovery system 102, the machine 104, the service center 106, and thetechnician device 108 of FIG. 1A. FIG. 1B further includes exampledatabase systems 110, 112, example machine connection process 114, andan example service device workflow 116. The example service deviceworkflow 116 includes example processes 118, 122, 124, 126, 128.

Initially, the service center 106 of FIG. 1B receives a service requestfrom the machine 104 and/or a user of the machine 104 (e.g., via aphone, computing device, and/or the machine 104 itself). The servicerequest can correspond to an problem that the machine 104 is having. Insome examples, the service request is received directly by the machinerecovery system 102. Once the service request is received, the servicerequest is analyzed to determine what the potential solution(s) are. Insome examples, the service center 102 utilizes database information fromthe enterprise system 110 and/or the service systems 112 to identifyparticular data related to the machine 104 (e.g., corresponding to theinitial context). Additionally, the machine recovery system 102 developspotential solutions, digital models, and/or relevant informationcorresponding to the service request based on knowledge corresponding toprior service requests. The initial context and information from theprior knowledge can be transmitted the technician device 108 as part ofa smart care package that is executed by the technician device toprovide relevant customized data corresponding to solutions and/orinformation needed to fix the machine 104.

As described above, the technician device 108 obtains a care packagecorresponding to information that may aid the technician device 108and/or a resource utilizing the technician device 108 in servicing themachine 104. In some examples, as shown in process 114, the techniciandevice 108 may be connected to the machine 104 to obtain system logs,continuous system health, and/or other analytics corresponding to themachine 104. Such data may be obtained prior to, during, and/or afterthe servicing is complete. Such data (e.g., machine data or image devicedata) may be transmitted to the service center 106 and/or machinerecovery system 102 to provide additional information that may beutilized to generate more accurate solution data, more accurate carepackage information, and/or better resource allocation (e.g.,dispatches) for future service requests. Such data, along with surveydata from the technician device 108 may be transmitted to the servicecenter 106 as part of the device workflow 116.

As shown in the device workflow 116, each process 118, 120, 124, 126,128 corresponds to data that may be used to create and/or improve theknowledge of the service center 106 for future service requests. Forexample, during the review process 118 information may be gatheredcorresponding to the machine 104 that may be used to triage and/ordiagnose the problem of the machine 104. The service center 106 can usesuch data to draw connections between symptoms, error codes, issues,etc., of a machine and solutions, care packages, and/or otherinformation needed to fix the machine 104. During the triage process120, information can be obtained corresponding to the issues, symptoms,etc. that correspond to a critical error or a non-critical error, forexample. Such information can be helpful for dispatching resources.During the diagnose process 122, information corresponding to digitaltwins, prior problem solution, implemented actions, etc., can beobtained by the service center 106 to optimize the diagnosis process forfuture service requests, for example. During the troubleshooting process124, information corresponding to troubleshooting techniques can beobtained by the service center 106 to determine which techniques workedor did not work for future service requests, for example. During theresolve/fix/dispatch process 126, resolution information can be obtainedby the service center 106 to update problem-solution information storedin a problem-solution database, for example. During the debrief/closeprocess 128, survey result information can be obtained by the servicecenter 106 from the technician device 108, for example. Such informationmay be used to update processes of the service center 106 for futureservice requests.

FIG. 2 is a block diagram of an example implementation of the machinerecovery system 102 of FIGS. 1A and/or 1B. The machine recovery system102 includes a smart symptom system 202, a smart care package system204, a smart dispatch system 206, a smart find system 208, and arecovery updater system 210. The machine recovery system 102 furtherincludes a device interface 212 to interface with deployed machines, theservice center 106, and/or technician devices (e.g., the techniciandevice 108) and a database interface 214 to interface with enterprisesystem(s) database 216, machine log database(s) 218, a problem solutiondatabase 220, and a dynamic system health database 222. The smartsymptom system 202 includes a symptom processor 224 and a filter 226.The smart care package system 204 includes a care package generator 228and a solution predictor 230. The smart dispatch system 206 includes aremote instruction executor 232, a technician selector 234 (e.g., aresource selector), and a weight multiplier 236. The smart find system208 includes a machine identifier 240 and a model generator 242. Therecovery updater system 210 includes a survey generator 244 and aninformation updater 246.

The smart symptom system 202 of FIG. 2 converts symptoms of amalfunctioning machine 104 into error codes and/or identified problems.For example, when a machine malfunctions, the operator of the machine104 may not be able to identify the problem. Rather, the operator mayonly be able to identify symptoms that correspond to malfunctioningmachine. Additionally or alternatively, the machine 104 can generate anerror code corresponding to a particular part of the machine (e.g.,which is stored in the machine log database 218), but not identify theproblem itself. The smart symptom system 202 identifies the problemsand/or error codes of the machine 104 based on the user-identifiedand/or machine-identified symptoms. The smart symptom system 202includes the symptom processor 224 and the filter 226, as furtherdescribed below.

The smart care package system 204 of FIG. 2 generates a smart carepackage for technicians and/or resources to aid in the repair of themachine. For example, once a problem is identified, the smart carepackage system 204 how to solve the problem, information that can behelpful in solving the problem, tools necessary to solve the problem,replacement parts that can be necessary to fix the problem, models thatthe technician can use to model possible fixes, etc. The care packagesystem 204 generates the care package to provide the information to adevice of the technician for use during/before a repair of the machine.The smart care package system 204 includes the care package generator228 and the solution predictor 230, as further described below.

The smart dispatch system 206 of FIG. 2 determines how to dispatchresources (e.g., technician(s), automated repair programs, etc.) to fixa problem(s) of a machine. For example, the smart dispatch system 206can look at the skill required to fix the machine, other machines in theclinical workflow that may also need to be serviced/checked, howcritical the repair is, how many technicians or other resources shouldbe available to service the request, the distance to the repair site,whether the solution can be performed remotely, etc. to select theoptimal technician(s)/resource(s) to dispatch to service the machine.Although the smart dispatch system 206 can be described in conjunctionwith technicians as resources, other resources can be included in thesmart dispatch process. For example, the smart dispatch system 206 candetermine that an automated repair program can be utilized as a resourceto implement instructions on the machine 104 to repair the machine usingsoftware patches, reboots, etc. A resource can include a computingdevice such as a laptop computer, tablet computer, smartphone, otherhandheld technician device, etc., that can connect to the machine 104 tofacilitate diagnosis and/or repair of the machine 104 and/or otherconnected component(s), for example. The smart dispatch system 206includes the remote instruction executor 232, the technician selector234, and the weight multiplier 236, as further described below.

The smart find system 208 of FIG. 2 identifies machines based oncontextual information (e.g., by comparing obtained informationcorresponding to the machine 104 to stored information in the enterprisesystem(s) database 216) and/or identifiers and determines whether theidentified machine corresponds to a fleet of similar machines and/ormachines deployed in similar environments. The smart find 208 can selectand/or generate an artificial intelligence model (e.g., digitaltwin/model) based on parameters similar to the fleet of machines and usefeedback to update the artificial intelligence model based on feedbackinformation for more accurate and efficient diagnosis/dispatch/packagegenerate for subsequent machine problems. Additionally, the smart find208 tracks problems of the fleet to flag common problems for preemptiveservices (e.g., to identify upcoming problems and/or any other overallsystem statistical analysis). The smart find 208 includes the machineidentifier 240 and the model generator 242, as further described below.

The updater system 210 of FIG. 2 updates information stored in theproblem solution database 220, the dynamic system health database 222,layout and/or displayed information of the smart care package, and/orany other database based on determined error code(s) and/or feedbackfrom a technician/resource once the service request is complete.Additionally, the updater system 120 can update various information usedby processes disclosed herein using artificial intelligence models basedon the technician/resource feedback. The machine recovery system 102builds knowledge corresponding to previous machine servicing to be ableto improve subsequent diagnoses, care packages, models/digital twins,dispatches, etc., based on the prior machine services using a neuralnetwork, for example. The updater system 210 includes the surveygenerator 244 and the information updater 246, as further describedbelow.

The device interface 212 of FIG. 2 is a structural component thatinterfaces with the machine 104 and/or service center 106. For example,when there is a problem with one of the deployed machines, the machine104 can automatically transmit information corresponding to theproblem/error directly to the device interface via a wired or wirelessnetwork communication. Additionally or alternatively, the machine 104can include an interface for a user to transmit problem and/or errorinformation to a service center 106 and/or directly to the deviceinterface 212. In some examples, a user of the machine 104 can determinethat the machine 104 is not functioning properly and call the servicecenter 106. In such an example, the service center 106 can include auser or processor that enters information regarding the machine 104and/or symptoms of the machine 104 that is transmitted to the deviceinterface 212. Additionally, the device interface 212 transmits carepackages to selected technician devices (e.g., the technician device108) to alert the technician/resource to the service request and providethe technician/resource with a smart care package corresponding to theinformation needed to service the machine. The device interface 212 alsocan transmit survey information to the technician device 108 to obtainfeedback from the technician/resource after a service has been complete.The response is received via the device interface 212 from thetechnician device 108.

The database interface 214 of FIG. 2 is a structural component thatinterfaces with the databases 216, 218, 220, 222. In some examples, thedatabase interface 214 obtains information from one or more of thedatabases 216, 218, 220, 222 via a wireless network communication. Themachine recovery system 102 can perform a smart symptom, generate asmart care package, initiate a smart dispatch, and perform a smart findbased on the information in the databases 216, 218, 220, 222. In someexamples, the database interface 214 interfaces with the one or moredatabases 216, 218, 220, 222 to update the information in thedatabase(s) 216, 218, 220, 222 based on identified error codes and/orfeedback information from technicians and/or resources.

The enterprise system(s) database 216 of FIG. 2 is one or more databasesand/or other data store/memory that store data corresponding to systemidentifiers, customer sites and layouts, contract warranties, assetlocations, and/or other contextual information, etc. The information inthe enterprise system(s) database 216 can initially be entered when acustomer installs a machine and can be updated periodically oraperiodically based on updates to information of the machine. Theenterprise system(s) database 216 can be located locally or can belocated remotely. For example, a machine can include an enterprisesystem that communicates with the database interface 214 via a wirelessnetwork communication or the machine 104 can periodically oraperiodically transmit the enterprise system information to theenterprise system(s) database 216. The machine recovery system 102 usessuch enterprise system information in conjunction with the smart findprocess, the smart symptom process, and/or the smart dispatch process.The enterprise system(s) database 216 can be one database forinformation of all deployed machines or the enterprise system(s)database 216 can be multiple databases corresponding to a customer,location, fleet, type, etc. The machine recovery system 102 uses suchcontextual information to determine symptom recognition from servicehistory data and the description of a problem from a user. Additionally,the machine recovery system 102 can use the database information of aparticular machine entered into the enterprise system database 216 for aparticular customer site/geographical area (e.g., zip code,neighborhood, address, etc.) to determine how to best dispatchtechnicians/resources.

The machine log(s) 218 of FIG. 2 is one or more databases and/or otherdata store/memory that store data corresponding to machine logs ofdeployed machines, system health information, identifiers, error traces,utilization information and usage information, for example. Theinformation in the machine log(s) 218 is provided by the deployedmachines. The machine log(s) 218 can be located locally or can belocated remotely. For example, the machine 104 can include a machine logat the machine 104 that communicates with the database interface 214 viaa wireless network communication or the machine 104 can transmitperiodically or aperiodically the machine log information to an offsitemachine log(s) database. The machine log(s) 218 can be one database forinformation of all deployed machines or the machine log(s) 218 can bemultiple databases corresponding to a customer, location, fleet, type,etc. The machine recovery system 102 uses such machine log informationin conjunction with the smart find process, the smart care packageprocess, and/or the smart symptom process. For example, the machinerecovery system 102 can correlate information that users havehistorically used to describe a particular system issue with machinelogs to develop an effective smart symptom to identify machine problems.Additionally, the machine recovery system 102 can identify system issuesas idiosyncrasy issues or fleet level issues by looking at machine loginformation to identify correlations when an issue is identified.

The problem solution database 220 of FIG. 2 is a database and/or otherdata store, memory, etc., that stores data corresponding to priorproblem and solutions correlations based on survey data from priorcompleted service requests. The problem solution database 220 can belocated locally or can be located remotely. The problem solutiondatabase 220 can be one database for information of all deployedmachines or the problem solution database 220 can be multiple databasescorresponding to a customer, location, fleet, type, etc. The machinerecovery system 102 uses such problem solution information inconjunction with the smart care package process, and/or the smartdispatch process. For example, the machine recovery system 102 canidentify manual sections needed, replacement parts needed, and/or toolsneeded for a particular problem based on prior survey information todevelop the smart care package. Additionally, the machine recoverysystem 102 can utilize problem solution information with the smartdispatch process whether problems can be categorized as a fleet problemor an idiosyncratic problem

The dynamic system health database 222 of FIG. 2 is a database and/orother data store, memory, etc., that stores data corresponding to thehealth of deployed machines (e.g., corresponding to age of a machine incomparison to similar machines, problems identified, etc.). The dynamicsystem health database 222 can be located locally or can be locatedremotely. The dynamic system health database 222 can be one database forinformation of all deployed machines or the dynamic system healthdatabase 222 can be multiple databases corresponding to a customer,location, fleet, type, etc. The machine recovery system 102 uses suchdynamic system health information in conjunction with the smart carepackage process and/or the smart symptom process. For example, themachine recovery system 102 can monitor the dynamic health of a machineas a service request is being reviewed to provide better capabilities ofeffecting smart logs and smart symptoms. By looking at the currenthealth of the machine, a better decision can be made by the machinerecovery system 102 on the actual problem and/or care package needed tosolve the issue. For example, the system health of machines can bemonitored by running a background daemon process that analyzes machiningdata in near real time and updating the system health in the dynamicsystem heath database 222 accordingly.

The symptom processor 224 of FIG. 2 receives identified symptoms fromthe machine 104 and/or service center 106 and uses known probleminformation (e.g., corresponding to data of the issue database 227)corresponding to the identified symptoms, machine logs, machineinformation, and dynamic health information to identify problems/issuesand/or error codes that correspond to the identified symptoms based onproblem solution information. For example, when a symptom is receivedfrom a user of a machine, the symptom processor 224 uses the health,machine information, and machine logs of the corresponding machine toidentify error codes/issues that correspond to the identified symptoms.To decrease the set of error codes/issues, the symptom processor 224 cantransmit a prompt for the user to identify whether or not adistinguishing symptom is present in the machine. A distinguishingsymptom is a symptoms that is in one or more of the error codes/issuesof the set and not in one or more error codes/issues of the set. Forexample, a symptom that is common to all error codes/issues in the setis not a distinguishing symptom. Additionally or alternatively, thesymptom processor 224 can interface will the device directly via thedevice interface 212 to execute one or more processes to identifywhether or not the distinguishing symptom is present in the machine.

Additionally or alternatively, the symptom processor 224 of FIG. 2 caninterface with the machine 108 (e.g., directly using the deviceinterface 212 and/or indirectly via the technician device 108, etc.) toobtain output data of the machine 104 (e.g., photos taken by the machine104, etc.). In such examples, the symptom processor 224 can process animage obtained by the imaging device to identify an artifact (e.g.,flaw) found on the image. The symptom processor 224 can uses identifiedflaws to identify a problem, a source of the problem, and/or additionalsymptoms. For example, the symptom processor 224 can, based on anidentified flaw, determine that that there is not enough radiation, toomuch radiation, identify a failing detector, misalignment issues, etc.In some examples, the symptom processor 224 can compare the identifiedproblem, source of problem, and/or symptoms associated with the flawwith previous problem-solution information (e.g., stored in the exampleproblem solution database 220, etc.), to determine if the problemcorresponds to a configuration and/or design flaw. In such examples, thesymptom processor 224 may transmit an alert to the machine 108, a userof the machine 108 (e.g., via text, email, etc.), a systemadministrator, etc. identify the design flaw. Additionally, the machineidentifier 240 may update fleet information and/or generate an alert tosimilar machines to identify the design flaw.

Once a response to the prompt to identify whether a distinguishingsymptom is present in the machine, the filter 226 of FIG. 2 can filterout a set of issues from the issue database 227 into a subset based onwhether or not distinguishing symptoms are present. For example, when afirst half of the error codes/issues of a set corresponds to adistinguishing symptom and a second half of the error codes/issues ofthe set do not correspond to the distinguishing symptom, then the filter226 filters out the first half of the set or the second half of the setto generate a subset based on whether a response to the promptidentifies whether or not the distinguishing feature is present in themachine.

The issue database 227 of FIG. 2 stores a listing of known issues.Additionally, the issue database 227 stores corresponding error codesand/or symptoms for the known issues. In some examples, when new issuesare identified by a technician/resource, the technician/resource of thetechnician device 108 and/or the technician device 108 itself can enterinformation corresponding to the new issues via the technician device108 and the issue database 227 update the stored data to include the newissues and corresponding error codes and/or symptoms. Additionally oralternatively, the machine 104 can transmit new error codes and/or a newcombination of error codes that the issue database 227 can store as anew issue. Additionally or alternatively, a manager/system coordinatorcan add additional issues and corresponding error codes and/or symptomsinto the issue database 227.

The care package generator 228 of FIG. 2 is a structural component thatgenerates a customized care package for a service technician/resourcewith regards to a requesting machine. The care package generator 228initially has a template care package that includes blank fields thatcan be customized based on the identified solution, machine information,etc. For example, when the location/type of the machine 104 to beserviced is known, the care package generator 228 can include mapscorresponding to location of the machine 104 and informationcorresponding to the type of machine. Additionally, the care packagegenerator 228 can provide more detailed and/or relevant informationbased on one or more identified error codes or one or more solutionspredicted by the solution predictor 230, as further described below. Forexample, when the solution predictor 230 determines that the solutioncorresponds to a solution related to a particular part of the machine,the care package generator 228 can include manual information related tothe particular part of the machine 104, replacement parts that can beneeded to service the machine, and/or special tools that can be neededto service the particular part and/or the particular solution. Thespecial tools can be tools that are not typically carried by alltechnicians (e.g., tools that can be included at a particular locationand need to be brought to the service request). The care packagegenerator 228 can determine whether replacement parts are needed toservice the machine 104 based on feedback from technicians of previousservice requests that can be stored in the problem solution database220. In some examples, the care package generator 228 can includedigital twins and/or network models (e.g., one or more artificialintelligence models) corresponding to the identified machine. Using thedigital twin(s) and/or artificial intelligence model(s), the techniciancan be able to virtually test the model and/or digital twin beforeservicing the machine 104 to help ensure that the actions of thetechnician will fix the machine. In some examples, when the care packagegenerator 228 determines that one or more of the solutions can beexecuted remotely, then the care package generator 228 can includeinstructions on how to service the machine 104 remotely and/or code thatcan be transmitted to the device to service the machine 104 remotely inthe care package.

The solution predictor 230 of FIG. 2 is a structural component thatpredicts solutions based on identified error codes/issues in conjunctionwith information in the problem solution database and/or system healthinformation. For example, the solution predictor 230 can identifypotential solutions by connecting the error codes/issues to othermachines based on previous solutions identified by technicians inprevious surveys, by applying the errors/issues in a digital twin/modeland simulating potential solutions, by identifying similar errorcodes/issues in other machines (e.g., based on similar models, similarcontextual information, similar fleet information, similar contextualinformation, etc.), and/or based on the system health of the machine.Additionally or alternatively, the solution predictor 230 may implementa neural network (e.g., such as the neural network 500 of FIG. 5) totransform error codes and/or issues with problem-solution informationand/or system health information to predict a solution. In suchexamples, the neural network may be trained using previous servicerequest data. The solution predictor 230 transmits the identifiedsolutions to the care package generator 228 to customize the carepackage based on the identified solutions.

The remote instruction executor 232 of FIG. 2 is a structural componentthat transmits instructions to the machine 104 (e.g., via the deviceinterface 212) to attempt to fix the problem remotely. For example, whenthe care package includes instructions corresponding to a potentialremote solution, then the remote instruction executor 232 can,automatically or based on instructions from a technician, transmit theinstructions to the device via the device interface 212. Thus, thedevice can execute the instructs to see whether the remote service fixedthe machine. Such remote instructions can include software updates,rebooting instructions, patches, fixes, etc.

The technician selector 234 of FIG. 2 is a structural component thatselects a technician to service the request based on a plurality offactors. The technicians and relevant information of the technicians arestored in the technician database 238, as further described below. Insome examples, the technician selector 234 can determine whether theservice request is critical or not (e.g., based on the severity of theerror codes/issues and/or based on the availability of alternativemachines in the hospital). When the service request is critical, thenthe technician selector 234 would only select one or more techniciansthat are immediately available to service the machine 104 (e.g., basedon the availability of the technicians and the location of thetechnicians with respect to the machine). In some examples, when theservice request is critical, then the technician selector 234 can alsotake into account the tools/replacement parts needed to service requestwhen determining the technician's distance to the machine. For example,when the service request requires a special tool/replacement partlocation in a particular location, then the technician selector 234takes into account the technician's distance to the location of thespecial tool/replacement part. Additionally or alternatively, thetechnician selector 234 selects one or more technicians to service therequest based on weights of the technician. The weights of thetechnicians correspond to how well the technician matches to the servicerequest and is determined using the weight multiplier 236, as furtherdescribed below. In some examples, the technician selector 234 analyzesthe clinical work flow to see whether there are additional machines thatshould be serviced, will soon need to be serviced (e.g., that can bepart of the problem), or are likely to have issues based on theinformation from the databases 216, 218, 220, 222. In some examples, thetechnician selector 234 may implement a neural network (e.g., theexample neural network 500 of FIG. 5) to select one or more resourcesbased on the above-inputs/resource characteristics. In such examples,the neural network may be trained based on the resources of the exampletechnician database 238 and/or previous service request data.

The weight multiplier 236 of FIG. 2 is a structural component thatweighs the available technicians for the service request. For example,the weight multiplier 236 can set an initial weight for each availabletechnician. The weight multiplier 236 adjusts the initial weights basedon various factors. For example, the factors can correspond to how manyof the machines of the service request that the technicians are capableof servicing, skill level of the technicians, tools that the technicianscurrently possesses, replacement parts that the technician currentlypossess (e.g., which can be identified by the technician using thedevice 108), distance to the service site taking into account distanceto pick up additional tools if needed, availability of the technicians,etc. The amount of weight adjustment for each factor can be based onuser, manufacturer, or customer preferences.

The technician database 238 of FIG. 2 is a database that includesup-to-date information corresponding to available technicians and/oravailable resources, schedules of the technicians, the skill levels ofthe technicians, the types of machines that the technicians and/or otherresources can service, the tools and/or replacement parts that thetechnician currently has in their possession, the location of thetechnicians, etc. The set of available technicians/resources can befiltered and weighted based on the information stored in the techniciandatabase 238. For example, if the problem cannot be fixed remotely, theautomated resources and/or instructions/software-based resources may befiltered out. The technician information can be updated periodically,aperiodically, and-or in real-time or near real-time based oninformation from the technician device 108 (e.g., via the deviceinterface 212).

The machine identifier 240 of FIG. 2 is a structural component thatidentifies a machine and associates the identified machine with a fleetof machines. For example, the machine identifier 240 can identify themachine 104 based on a machine identifier (e.g., an imaging deviceidentifier, etc.) provided by the machine 104 and/or a user of themachine 104 or the machine identifier 240 can identify the machine 104based on contextual information (e.g., by comparing location of themachine, type of machine, database information corresponding to auniverse of the machines, etc., to stored system information of theenterprise system(s) database 216). In some examples, the machineidentifier 240 can identify the machine 104 based on a photo submittedby a user, taken by the machine 104, taken by the technician device 108,etc., using image processing techniques. In such examples, the machineidentifier 240 can compare the characteristics of the image to referenceimages to identify the machine 104 based on a matching reference imageand/or a determined location based on the photo of the machine (e.g., byidentifying the particular machine 104 based on its characteristicsidentified in the image, identifying its location based on features ofthe environment surrounding the machine 104 captured in the photo,etc.). Once the identifier is determined, the machine identifier 240determines the make, model, modality, and/or other information of theidentified machine by comparing the identifier to machine informationstored in a database (e.g., the enterprise system(s) database 216,etc.). Additionally, the machine identifier 240 determines whether thereis a corresponding fleet of machines. In some examples, the machineidentifier 240 may implement a neural network (e.g., the example neuralnetwork 500 of FIG. 5) to identify a fleet based on an identifiedmachine. In such examples, the neural network may be trained based onmachine information corresponding to the universe of machines. A fleetof machines corresponds to a group of machines that have similarcontextual information. The fleet of machines can correspond to anycombination of a similar location, a similar model, a similar type, asimilar climate, a similar age, similar features, etc. In some examples,a machine can correspond to multiple different fleets. Each fleet canhave an artificial intelligence model (e.g., neural network model,digital twin, and/or other machine/deep learning construct, etc.) thatcorresponds to the fleet for applying techniques (e.g., for testingpurposes) and/or identifying which solutions can solve the problem.Additionally, the machine identifier 240 can store and flag commonproblems among the fleet. For example, when an issue arises among amachine in the fleet, then the machine identifier 240 can store theissue as long as when the issue occurred, how long the machine 104 wasoperable when the issue occurred, the time from the last service etc.When the machine identifier 240 determines that the same issue occursmore than a threshold number of times in the same fleet, then themachine identifier 240 can flag the issue and transmit a warningcorresponding to the flag to identify that the issue can occur in othermachines of the fleet. For example, when the machine identifier 240determines that three machines in a fleet all experienced the same issueafter three years of operation, the machine identifier 240 can transmitwarnings related to any machine in the fleet that has been operating foraround three years. Additionally, the machine identifier 240 may updatefleet information and/or generate an alert to similar machines based onissues identified by other processes (e.g., a design flaw, etc.) of theexample machine recovery system 102.

The model generator 242 of FIG. 2 is a structural component to generatean artificial intelligence model (e.g., network model/digital twin,other machine/deep learning construct, etc.) when the device does notcorrespond to one of the fleets (e.g., does not correspond to an alreadyavailable artificial intelligence model). A new fleet is created withthe new artificial intelligence model and subsequent machines thatcorrespond to the new fleet can be added to the fleet. The artificialintelligence model corresponds to the machine information (e.g., model,age, usage profile, etc.).

The survey generator 244 of FIG. 2 is a structural component thatgenerates a survey (e.g., a prompt, flag, etc.) for a technician. Thesurvey is provided to the technician device 108 to obtain responses fromthe technician after the service request is complete. In some examples,the survey is preloaded onto the technician device 108. The surveyincludes information related to whether the care package included allthe relevant information, what information was not relevant the layoutof the smart care package, were any replacement parts needed and, if so,which parts, which information should've been included but was not,which tools were used, was the identified solutions correct, where thereany unexpected issues, and/or any other information that can generatemore accurate smart dispatch, smart symptom, smart care package, and/orsmart find processes.

The information updater 246 of FIG. 2 updates the information in theproblem solution database 220 and/or the dynamic system health database222 based on the information from the response to the survey/prompt fromthe technician. Additionally, the information updater 246 can update thetechnician database 238 based on changes of the technicians (e.g.,location, tools, etc.). The information updater 246 can utilize and/orinclude artificial intelligence model(s) (e.g., digital twin(s), neuralnetwork(s), etc.) to perform machine learning processes to tune andimprove the smart symptom, smart care package, smart dispatch, and smartfind processes.

Artificial Intelligence Models

In certain examples, a target device or machine to be evaluated/repaired(e.g., an imaging device, an imaging workstation, a health informationsystem, etc.), a resource (e.g., a technician and/or other user, atechnician's device, care package, etc.), a fleet of machines, etc., canbe modeled as a digital twin and/or processed according to an artificialneural network and/or other machine/deep learning network model(referred to herein as “artificial intelligence models”). Using one ormore artificial intelligence models, such as a digital twin, neuralnetwork model, etc., one or more real-life system can be modeled,monitored, simulated, and prepared for field force automationmanagement.

Digital Twin Examples

A digital representation, digital model, digital “twin”, or digital“shadow” is a digital informational construct about a physical system,process, etc. That is, digital information can be implemented as a“twin” of a physical device/system/person/process and informationassociated with and/or embedded within the physicaldevice/system/process. The digital twin is linked with the physicalsystem through the lifecycle of the physical system. In certainexamples, the digital twin includes a physical object in real space, adigital twin of that physical object that exists in a virtual space, andinformation linking the physical object with its digital twin. Thedigital twin exists in a virtual space corresponding to a real space andincludes a link for data flow from real space to virtual space as wellas a link for information flow from virtual space to real space andvirtual sub-spaces.

For example, FIG. 3 illustrates a machine or device such as an imagingdevice, radiology workstation, information system, etc.; a resource suchas a technician, other user, a computing device, care package, etc.;and/or other item 310 in a real space 315 providing data 320 to adigital twin 330 in a virtual space 335. The digital twin 330 and/or itsvirtual space 335 provide information 340 back to the real space 315.The digital twin 130 and/or virtual space 335 can also provideinformation to one or more virtual sub-spaces 350, 352, 354. As shown inthe example of FIG. 3, the virtual space 335 can include and/or beassociated with one or more virtual sub-spaces 350, 352, 354, which canbe used to model one or more parts of the digital twin 330 and/ordigital “sub-twins” modeling subsystems/subparts of the overall digitaltwin 330.

Sensors connected to the physical object (e.g., the device/resource 310)can collect data and relay the collected data 320 to the digital twin330 (e.g., via one or more device sensors, self-reporting, output fromone or more system components such as the device interface 212, thedatabase interface 214, the symptom processor 224, the filter 226, thecare package generator 228, the solution predictor 230, the remoteinstruction executor 232, the technician selector 234, the weightmultiplier 236, the technician database 238, the machine identifier 240,the model generator 242, the survey generator 224, the informationupdater 246, the database interface 246, and/or, more generally, theexample machine recovery system 102, and/or combination thereof, etc.).Interaction between the digital twin 330 and the device/resource 310 canhelp improve detection of a symptom, determine of an issue, resolutionof the issue, configuration of a tool, etc. An accurate digitaldescription 330 of the device/resource/item 310 benefiting from areal-time or substantially real-time (e.g., accounting from datatransmission, processing, and/or storage delay) allows the system 100 topredict “failures” in machine/device operation, accuracy, outcome,communication, etc.

In certain examples, sensor data, technical support, obtained images,test results, etc., can be used in augmented reality (AR) applicationswhen a technician is examining, diagnosing, and/or otherwise fixing amachine, device, etc. (e.g., the machine 104). Using AR, the digitaltwin 330 follows the machine's response to the interaction with thetechnician, the technician device 108 and/or machine recovery system102, for example.

Thus, rather than a generic model, the digital twin 330 is a collectionof actual physics-based models reflecting the device/resource 310 (e.g.,the machine 104, machine recovery system 102, technician device, etc.)and its associated norms, conditions, etc. In certain examples,three-dimensional (3D) modeling of the device/resource 310 creates thedigital twin 330 for the device/resource 310. The digital twin 330 canbe used to view a status of the device/resource 310 based on input data320 dynamically provided from a source (e.g., from the device/resource310, technician, health information system, sensor, etc.).

In certain examples, the digital twin 330 of the device/resource 310 canbe used for monitoring, diagnostics, and prognostics for thedevice/resource 310. Using sensor data in combination with historicalinformation, current and/or potential future conditions of thedevice/resource 310 can be identified, predicted, monitored, etc., usingthe digital twin 330. Causation, escalation, improvement, etc., can bemonitored via the digital twin 330. Using the digital twin 330, thedevice/resource's 310 behaviors can be simulated and visualized fordiagnosis, treatment, monitoring, maintenance, etc.

In contrast to computers, humans do not process information in asequential, step-by-step process. Instead, people try to conceptualize aproblem and understand its context. While a person can review data inreports, tables, etc., the person is most effective when visuallyreviewing a problem and trying to find its solution. Typically, however,when a person visually processes information, records the information inalphanumeric form, and then tries to re-conceptualize the informationvisually, information is lost and the problem-solving process is mademuch less efficient over time.

Using the digital twin 330, however, allows a person and/or system toview and evaluate a visualization of a situation (e.g., adevice/resource 310 and associated operational problem, etc.) withouttranslating to data and back. With the digital twin 330 in commonperspective with the actual device/resource 310, physical and virtualinformation can be viewed together, dynamically and in real time (orsubstantially real time accounting for data processing, transmission,and/or storage delay). Rather than reading a report, a technician canview and simulate with the digital twin 330 to evaluate a condition,progression, possible treatment, etc., for the device/resource 310. Incertain examples, features, conditions, trends, indicators, traits,etc., can be tagged and/or otherwise labeled in the digital twin 330 toallow the technician to quickly and easily view designated parameters,values, trends, alerts, etc.

The digital twin 330 can also be used for comparison (e.g., to thedevice/resource 310, to a “normal”, standard, or reference device orresource, set of operating criteria/symptoms, best practices, protocolsteps, etc.). In certain examples, the digital twin 330 of thedevice/resource 310 can be used to measure and visualize an ideal or“gold standard” value state for that device/resource, a margin for erroror standard deviation around that value (e.g., positive and/or negativedeviation from the gold standard value, etc.), an actual value, a trendof actual values, etc. A difference between the actual value or trend ofactual values and the gold standard (e.g., that falls outside theacceptable deviation) can be visualized as an alphanumeric value, acolor indication, a pattern, etc.

Further, the digital twin 330 of the machine/device 104 to be repaired,the technician device 108 to facilitate diagnosis/repair, etc., canfacilitate collaboration among systems, technicians, etc. Using thedigital twin 330, conceptualization of the device/resource 310 and itsstatus/configuration can be shared for evaluation, modification,discussion, etc. Collaborating entities do not need to be in the samelocation as the device/resource 310 and can still view, interact with,and draw conclusions from the same digital twin 330, for example.

Thus, the digital twin 330 can be defined as a set of virtualinformation constructs that describes (e.g., fully describes) the deviceresource 310 from a micro level (e.g., source, detector, positioner,processor, storage, etc.) to a macro level (e.g., whole imaging device,image capture subsystem, image analysis subsystem, patient monitoringsubsystem, etc.). In certain examples, the digital twin 330 can be areference digital twin (e.g., a digital twin prototype, etc.) and/or adigital twin instance. The reference digital twin represents aprototypical or “gold standard” model of the device/resource 310 or of aparticular type/category of device/resource 310, while one or morereference digital twins represent particular device(s)/resource(s) 310.Thus, the digital twin 130 of an x-ray imaging device can be implementedas a child reference digital twin organized according to certainstandard or “typical” x-ray imaging device characteristics, with aparticular digital twin instance representing the particular x-rayimaging device and its configuration, for example. In certain examples,multiple digital twin instances can be aggregated into a digital twinaggregate (e.g., to represent an accumulation or combination of multiplex-ray machines in a fleet sharing a common reference digital twin,etc.). The digital twin aggregate can be used to identify differences,similarities, trends, etc., between machines represented by theparticular digital twin instances, for example.

In certain examples, the virtual space 335 in which the digital twin 330(and/or multiple digital twin instances, etc.) operates is referred toas a digital twin environment. The digital twin environment 335 providesan integrated, multi-domain physics-based application space in which tooperate the digital twin 330. The digital twin 330 can be analyzed inthe digital twin environment 335 to predict future behavior, condition,progression, etc., of the device/resource 310, for example. The digitaltwin 330 can also be interrogated or queried in the digital twinenvironment 335 to retrieve and/or analyze current information 340, pasthistory, etc.

In certain examples, the digital twin environment 335 can be dividedinto multiple virtual spaces 350-354. Each virtual space 350-354 canmodel a different digital twin instance and/or component of the digitaltwin 330 and/or each virtual space 350-354 can be used to perform adifferent analysis, simulation, etc., of the same digital twin 330.Using the multiple virtual spaces 350-354, the digital twin 330 can betested inexpensively and efficiently in a plurality of ways whilepreserving device/resource operation and patient safety. A techniciancan then understand how the device/resource 310 can react to a varietyof adjustments in a variety of scenarios, for example.

In certain examples, the digital twin 330 can also model a space, suchas an operating room, surgical center, pre-operative preparation room,post-operative recovery room, etc. By modeling an environment, such as asurgical suite, the environment can be made, safer, more reliable,and/or more productive for patients, healthcare professionals (e.g.,surgeons, nurses, anesthesiologists, technicians, etc.). For example,the digital twin 330 can be used to evaluate environmental factors(e.g., spacing, usage patterns, other equipment, etc.) that can impactmachine 104 operation, etc.

In certain examples, a device, such as an optical head-mounted display(e.g., Google Glass, etc.) can be used with augmented reality to provideadditional information with respect to a machine 104 being viewed,information for repair, suggestions for diagnosis, etc. The device canbe used to pull in machine and/or technician device details, modeled viathe digital twin 330 and verified according to manufacturer'sspecifications, customer machine configuration, protocol, personnelpreferences, etc.

As shown in the example of FIG. 4A, an optical head-mounted display 400can include a scanner or other sensor 410 that scans items in its fieldof view (e.g., scans barcodes, radiofrequency identifiers (RFIDs),visual profile/characteristics, etc.). In some examples, thehead-mounted display 400 can be and/or can be used in conjunction withthe technician device 108 of FIGS. 1A and/or 1B. Item identification,photograph, video feed, etc., can be provided by the scanner 410 to thedigital twin 330, for example. The scanner 410 and/or the digital twin330 can identify and track items within range of the scanner 410, forexample. The digital twin 330 can then model the viewed environmentand/or objects in the viewed environment based at least in part on inputfrom the scanner 410, for example.

Alternatively or in addition, the digital twin 330 can be leveraged viaa technician device 450, such as a smartphone, tablet computer, laptopcomputer, etc. In some examples, the example technician device 450 canbe and/or can be used in conjunction with the technician device 108 ofFIGS. 1A and/or 1B. As shown in FIG. 4B, the technician device 450 canreceive input from a user, the machine recovery system 102, otherdiagnostic device, health information system, etc., and provide outputvia display to the technician, communication with the machine 104 underreview, etc. For example, the technician device 450 can provide thetechnician with suggestions to diagnose and/or repair the machine 104.The technician device 450 can also be used to interact with the machine104 to diagnose and/or repair issue(s) with the machine 104 and/or arelated component, for example. As shown in the example of FIG. 4B, thetechnician device 450 can provide one or more diagnostic and/or repairsuggestions 452 via a display screen 454 of the device. The exampletechnician repair device 450 can also communicate 456 (e.g., wirelesslyand/or via a wired connection) with the machine 104 to extractdiagnostic information, transmit configuration setting(s), etc.

FIG. 4C illustrates an example in which suggestions, communication,prompts, other information and/or interaction, etc., is provided via asmartwatch 480 to the technician at home, en route, and/or on site. Forexample, one or more suggestions 482 are provided via display screen 484of the smartwatch 480. In some examples, the example head-mounteddisplay 480 can be and/or can be used in conjunction with the techniciandevice 108 of FIGS. 1A and/or 1B.

Machine and/or Deep Learning Network Models

Machine learning techniques, whether deep learning networks or otherexperiential/observational learning systems, can be used to modelinformation together with and/or separate from the digital twin 330 toanalyze and/or predict a problem, issue, error, malfunction, etc., in asystem based on log data and/or other symptom(s), predict result ofparticular solution(s) on particular machine(s), etc. Deep learning is asubset of machine learning that uses a set of algorithms to modelhigh-level abstractions in data using a deep graph with multipleprocessing layers including linear and non-linear transformations. Whilemany machine learning systems are seeded with initial features and/ornetwork weights to be modified through learning and updating of themachine learning network, a deep learning network trains itself toidentify “good” features for analysis. Using a multilayeredarchitecture, machines employing deep learning techniques can processraw data better than machines using conventional machine learningtechniques. Examining data for groups of highly correlated values ordistinctive themes is facilitated using different layers of evaluationor abstraction.

Deep learning is a class of machine learning techniques employingrepresentation learning methods that allows a machine to be given rawdata and determine the representations needed for data classification.Deep learning ascertains structure in data sets using backpropagationalgorithms which are used to alter internal parameters (e.g., nodeweights) of the deep learning machine. Deep learning machines canutilize a variety of multilayer architectures and algorithms. Whilemachine learning, for example, involves an identification of features tobe used in training the network, deep learning processes raw data toidentify features of interest without the external identification.

Deep learning in a neural network environment includes numerousinterconnected nodes referred to as neurons. Input neurons, activatedfrom an outside source, activate other neurons based on connections tothose other neurons which are governed by the machine parameters. Aneural network behaves in a certain manner based on its own parameters.Learning refines the machine parameters, and, by extension, theconnections between neurons in the network, such that the neural networkbehaves in a desired manner.

Deep learning that utilizes a convolutional neural network (CNN)segments data using convolutional filters to locate and identifylearned, observable features in the data. Each filter or layer of theCNN architecture transforms the input data to increase the selectivityand invariance of the data. This abstraction of the data allows themachine to focus on the features in the data it is attempting toclassify and ignore irrelevant background information.

Alternatively or in addition to the CNN, a deep residual network can beused. In a deep residual network, a desired underlying mapping isexplicitly defined in relation to stacked, non-linear internal layers ofthe network. Using feedforward neural networks, deep residual networkscan include shortcut connections that skip over one or more internallayers to connect nodes. A deep residual network can be trainedend-to-end by stochastic gradient descent (SGD) with backpropagation,for example.

Deep learning operates on the understanding that many datasets includehigh level features which include low level features. While examining animage of an item, for example, rather than looking for an object, it ismore efficient to look for edges which form motifs which form parts,which form the object being sought. These hierarchies of features can befound in many different forms of data such as speech and text, etc.

Learned observable features include objects and quantifiableregularities learned by the machine during supervised learning. Amachine provided with a large set of well classified data is betterequipped to distinguish and extract the features pertinent to successfulclassification of new data.

A deep learning machine that utilizes transfer learning may properlyconnect data features to certain classifications affirmed by a humanexpert. Conversely, the same machine can, when informed of an incorrectclassification by a human expert, update the parameters forclassification. Settings and/or other configuration information, forexample, can be guided by learned use of settings and/or otherconfiguration information, and, as a system is used more (e.g.,repeatedly and/or by multiple users), a number of variations and/orother possibilities for settings and/or other configuration informationcan be reduced for a given situation.

An example deep learning neural network can be trained on a set ofexpert classified data, for example. This set of data builds the firstparameters for the neural network, and this would be the stage ofsupervised learning. During the stage of supervised learning, the neuralnetwork can be tested whether the desired behavior has been achieved.

Once a desired neural network behavior has been achieved (e.g., amachine has been trained to operate according to a specified threshold,etc.), the machine can be deployed for use (e.g., testing the machinewith “real” data, etc.). During operation, neural networkclassifications can be confirmed or denied (e.g., by an expert user,expert system, reference database, etc.) to continue to improve neuralnetwork behavior. The example neural network is then in a state oftransfer learning, as parameters for classification that determineneural network behavior are updated based on ongoing interactions. Incertain examples, the neural network can provide direct feedback toanother process. In certain examples, the neural network outputs datathat is buffered (e.g., via the cloud, etc.) and validated before it isprovided to another process.

Deep learning machines using convolutional neural networks (CNNs) can beused for data analysis. Stages of CNN analysis can be used for facialrecognition in natural images, computer-aided diagnosis (CAD), objectidentification and tracking, evaluation of symptoms, evaluation ofremediation outcomes, etc.

Deep learning machines can provide computer aided detection support toimprove item identification, relevance evaluation, and tracking, forexample. Supervised deep learning can help reduce susceptibility tofalse classification, for example. Deep learning machines can utilizetransfer learning when interacting with technicians to counteract thesmall dataset available in the supervised training. These deep learningmachines can improve their protocol adherence over time through trainingand transfer learning.

FIG. 5 is a representation of an example deep learning neural networkmodel 500 that can be used to implement the digital twin 330, work withthe digital twin 330 to provide suggestions for diagnosis and/or repair,and/or operate instead of the digital twin 330 to generate suggestedissues based on input symptoms, provide suggested repairs based on inputmachine configuration and problem information, etc. The example neuralnetwork 500 includes layers 520, 540, 560, and 580. The layers 520 and540 are connected with neural connections 530. The layers 540 and 560are connected with neural connections 550. The layers 560 and 580 areconnected with neural connections 570. Data flows forward via inputs512, 514, 516 from the input layer 520 to the output layer 580 and to anoutput 590. In some examples, the neural network model 500 is includedin and/or utilized by one or more of the components the machine recoverysystem 102 of FIG. 2.

The layer 520 is an input layer that, in the example of FIG. 5, includesa plurality of nodes 522, 524, 526. The layers 540 and 560 are hiddenlayers and include, the example of FIG. 5, nodes 542, 544, 546, 548,562, 564, 566, 568. The neural network 500 can include more or lesshidden layers 540 and 560 than shown. The layer 580 is an output layerand includes, in the example of FIG. 5, a node 582 with an output 590.Each input 512-516 corresponds to a node 522-526 of the input layer 520,and each node 522-526 of the input layer 520 has a connection 530 toeach node 542-548 of the hidden layer 540. Each node 542-548 of thehidden layer 540 has a connection 550 to each node 562-568 of the hiddenlayer 560. Each node 562-568 of the hidden layer 560 has a connection570 to the output layer 580. The output layer 580 has an output 590 toprovide an output from the example neural network 500.

Of connections 530, 550, and 570 certain example connections 532, 552,572 can be given added weight while other example connections 534, 554,574 can be given less weight in the neural network 500. Input nodes522-526 are activated through receipt of input data via inputs 512-516,for example. Nodes 542-548 and 562-568 of hidden layers 540 and 560 areactivated through the forward flow of data through the network 500 viathe connections 530 and 550, respectively. Node 582 of the output layer580 is activated after data processed in hidden layers 540 and 560 issent via connections 570. When the output node 582 of the output layer580 is activated, the node 582 outputs an appropriate value based onprocessing accomplished in hidden layers 540 and 560 of the neuralnetwork 500.

While an example implementation of the machine recovery system 102 ofFIGS. 1A and/or 1B is illustrated in FIG. 2, one or more of theelements, processes and/or devices illustrated in FIG. 2 can becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the device interface 212, the databaseinterface 214, the symptom processor 224, the filter 226, the carepackage generator 228, the solution predictor 230, the remoteinstruction executor 232, the technician selector 234, the weightmultiplier 236, the technician database 238, the machine identifier 240,the model generator 242, the survey generator 224, the informationupdater 246, the database interface 246, and/or, more generally, themachine recovery system 102 of FIG. 2 can be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the device interface 212, thedatabase interface 214, the symptom processor 224, the filter 226, thecare package generator 228, the solution predictor 230, the remoteinstruction executor 232, the technician selector 234, the weightmultiplier 236, the technician database 238, the machine identifier 240,the model generator 242, the survey generator 224, the informationupdater 246, the database interface 246, and/or, more generally, themachine recovery system 102 of FIG. 2 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), graphics processing unit(s)(GPU(s)), digital signal processor(s) (DSP(s)), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the device interface212, the database interface 214, the symptom processor 224, the filter226, the care package generator 228, the solution predictor 230, theremote instruction executor 232, the technician selector 234, the weightmultiplier 236, the technician database 238, the machine identifier 240,the model generator 242, the survey generator 224, the informationupdater 246, and/or the database interface 246 is/are hereby expresslydefined to include a non-transitory computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the machine recovery system 102 of FIG. 2 can include oneor more elements, processes and/or devices in addition to, or insteadof, those illustrated in FIG. 2, and/or can include more than one of anyor all of the illustrated elements, processes and devices. As usedherein, the phrase “in communication,” including variations thereof,encompasses direct communication and/or indirect communication throughone or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the machine recovery system 102 ofFIG. 2 are shown in FIGS. 6-10. The machine readable instructions can bean executable program or portion of an executable program for executionby a computer processor such as the processor 1112 shown in theprocessor platform 800 discussed below in connection with FIG. 11. Theprogram can be embodied in software stored on a non-transitory computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, aDVD, a Blu-ray disk, or a memory associated with the processor 1112, butthe entire program and/or parts thereof could alternatively be executedby a device other than the processor 1112 and/or embodied in firmware ordedicated hardware. Further, although the example program is describedwith reference to the flowcharts illustrated in FIGS. 6-10, many othermethods of implementing the machine recovery system 102 canalternatively be used. For example, the order of execution of the blockscan be changed, and/or some of the blocks described can be changed,eliminated, or combined. Additionally or alternatively, any or all ofthe blocks can be implemented by one or more hardware circuits (e.g.,discrete and/or integrated analog and/or digital circuitry, an FPGA, anASIC, a comparator, an operational-amplifier (op-amp), a logic circuit,etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 6-10 can beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.can be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

FIG. 6 includes a flowchart 600 representative of machine readableinstructions that can be implemented by the structural components of themachine recovery system 102 of FIG. 2. Although the flowchart 600 ofFIG. 6 is described in conjunction with the machine recovery system 102of FIGS. 1 and/or 2, other type(s) of recovery system(s) and/or othertype(s) of processor(s) can be utilized instead.

At block 602, the smart symptom system 202 of the machine recoverysystem 102 performs the smart symptom process. As further describedbelow in conjunction with FIG. 7, the smart symptom system 202 convertssymptoms of a machine to issues and/or error codes for to service themachine. At block 604, the smart care package system 204 of the machinerecover system 102 performs the smart care package process. As furtherdescribed below in conjunction with FIG. 8, the smart care packagesystem 204 generates a smart care package to the device 108 of thetechnician to service the machine 104. At block 606, the smart dispatchsystem 206 of the machine recover system 102 performs the smart dispatchprocess. As further described below in conjunction with FIG. 9, thesmart dispatch system 206 performs the smart dispatch to select one ormore technicians to service the machine.

At block 608, the smart find system 208 of the machine recover system102 determines whether the smart find process has been performed withinone of the previous processes (e.g., the smart symptom, the smart carepackage, and/or the smart dispatch processes). When the smart findprocess was not performed during the initial processes, then the smartfind can be performed in conjunction with the technician servicing therequest. For example, the technician can enter information into thetechnician device 108 during the servicing to identify the machine. Whenthe smart find system 208 determines that a smart find process has beenperformed (block 608: YES), then the process continues to block 612.When the smart find system 208 determines that a smart find process hasnot been performed (block 608: NO), then the smart find system 208performs a smart find process (block 610), as further described below inconjunction with FIG. 10.

At block 612, the survey generator 244 determines whether the servicerequest is complete. For example, when the technician completes aservice request, the technician can identify the completion via thetechnician device 108, which transmits a completion trigger via thedevice interface 212. The survey generator 244 determines that theservice request is complete in response to receiving the completiontrigger. When the survey generator 244 determines that the servicerequest is not complete (block 612: NO), then the process returns toblock 612 until the service request is complete. When the surveygenerator 244 determines that the service request is complete (block612: YES), then the survey generator 244 transmits a generated survey tothe technician device 108 via the device interface 212 (block 614).Alternatively, the prompt may be preloaded into the technician device108. In such an example, the survey generator 244 does not transmit thesurvey. Rather, the survey generator 244 determines that the servicerequest is complete when the feedback from the preloaded prompt isreceived at the machine recovery system 102. At block 616, theinformation updater 246 obtains feedback information based on the surveyresults entered by the technician (e.g., via the device interface 212).At block 618, the information updater 246 updates the problem solutioninformation in the problem solution database 220, smart care packageformat and/or provided information, and/or the artificial intelligencemodel(s) (models/digital twins, neural networks, etc.) based on thefeedback using the artificial network model(s) to perform machinelearning. For example, the neural network 800 of FIG. 8 can be utilizedto implement the digital twin 630, work with the digital twin 630 toprovide suggestions for diagnosis and/or repair, and/or operate insteadof the digital twin 630 to generate suggested issues based on inputsymptoms, provide suggested repairs based on input machine configurationand problem information, etc. to update data in the databases 216, 218,220, 222, 238 and/or update the digital twins and/or optimize the smartsymptom process, smart care package process, smart dispatch process,and/or smart find process for future service requests by applying theobtained feedback data to the neural network (e.g., to further train theneural network).

FIG. 7 includes a flowchart 602 representative of machine readableinstructions that can be implemented by the structural components of themachine recovery system 102 of FIG. 2 to perform a smart symptomprocess. Although the flowchart 602 of FIG. 7 is described inconjunction with the machine recovery system 102 of FIGS. 1 and/or 2,other type(s) of recovery system(s) and/or other type(s) of processor(s)can be utilized instead.

At block 702, the symptom processor 224 obtains a service request from auser via the device interface 212. For example, a user can call into aservice center 106 that transmits the service request to the deviceinterface 212 or the user can enter the service request on a userinterface on a computer or on the machine 104 itself that istransmitted, directly or indirectly, to the device interface 212. Atblock 704, the machine identifier 240 determines whether a machineidentifier or image device identifier and/or contextual information isavailable for the machine. For example, when the user interfaces with auser interface on the machine 104 and the machine 104 transmits theservice request, then the machine 104 can include additional informationincluding the machine/imaging device ID and/or contextual information.

If the machine identifier 240 determines that the machine/imaging deviceID or contextual information is not available (block 704: NO), then theprocess continues to block 708. When the machine identifier 240determines that the machine/imaging device ID or contextual informationis available (block 704: YES), then the machine recovery system 102performs the smart find process (block 706), as further described belowin conjunction with FIG. 10. At block 708, the symptom processor 224obtains one or more symptoms from the user via the device interface 212.The one or more symptoms can be included in the service request. In someexamples, the service request may not include any symptoms. At block710, the symptom processor 224 obtains error log (e.g., logs identifyingone or more error codes) information from the machine log database 218corresponding to the machine. At block 712, the filter 226 filters outissues that do not correspond to the error logs and/or symptoms togenerate a subset of issues that correspond to the error logs and/orsymptoms. The filter 226 obtains the issues from the issue database 227.

At block 714, the symptom processor 224 identifies distinguishingsymptoms of remaining issues. For example, the symptom processor 224 canidentify a distinguishing symptom to split the issues into first issuesthat correspond to the distinguishing symptom and second issues that donot correspond to the distinguishing symptoms. The symptom processor 224can identify whether one or more of the distinguishing features ispresent in the machine 104, and the filter 226 can filter out the firstor second issues based on system feedback and/or user response to reducethe number of potential issues that a technician is to check/fix toservice for the machine 104.

At block 716, the symptom processor 224 selects a distinguishingsymptom. The symptom processor 224 can select a symptom that is mostdistinguishing (e.g., a symptom that corresponds to as close to half ofthe remaining issues). At block 718, the symptom processor 224 transmitsa prompt to the machine, a user and/or a user device (e.g., thecomputing device used by the user to transmit data corresponding to themachine) based on the distinguishing symptom via the device interface212. The prompt can be sent directly to the machine 104 and/or to theservice center 106 (e.g., which then instructs the machine, the user,and/or user device based on the prompt). In some examples, the prompt ispreloaded on the technician device 108. The prompt requests a responsefrom the machine, user, and/or technician device 108 to identify whetherthe selected distinguishing symptom is present and/or to perform aprocess to see whether the distinguishing symptom is present. Forexample, the prompt can include instructions to be performedautomatically by the machine for the machine to provide additionalfeedback corresponding to additional symptoms (e.g., instructions toexecute one or more processes and provide the results of the one or moreprocesses as feedback). In another example, the prompt can identifydistinguishing symptoms to a user device (e.g., a computer or computingdevice) that prompts the user for additional feedback corresponding tothe distinguishing symptom. In some examples, the technician device 108can obtain data directly or indirectly from the machine 104 and includethe obtained data in the feedback (e.g., input data and/or output datafrom different parts of the machine 104 taken before, during, and/orafter the servicing).

At block 720, the symptom processor 224 obtains a response from the user(e.g., directly via the machine 104 or via the service center 106)and/or from machine (e.g., without input from the user) via the deviceinterface 212. The response includes data related to whether or not thedistinguishing symptom is present at the machine. At block 722, thefilter 226 filters out issues based on the response. For example, whenthe response indicates that the distinguishing symptom is present in themachine, then the filter 226 filters out the issues that do notcorrespond to the distinguishing symptom and when the response indicatesthat the distinguishing symptom is not present in the machine, thefilter 226 filters out the issues that do correspond to thedistinguishing symptom.

At block 724, the symptom processor 224 determines whether more than athreshold number of issues are remaining. The threshold number of issuescan be based on user and/or manufacturer preferences, for example. Whenthe symptom processor 224 determines that there are not more than athreshold number of issues remaining (block 724: NO), then the processreturns to block 604 of FIG. 6. When the symptom processor 224determines that there are more than a threshold number of issuesremaining (block 724: YES), then the symptom processor 224 determineswhether there are additional distinguishing symptoms remaining (block726). When the symptom processor 224 determines that there areadditional distinguishing symptoms remaining (block 726: YES), then theprocess returns to block 716 to further filter out issues. When thesymptom processor 224 determines that there are no additionaldistinguishing symptoms remaining (block 726: NO), then the processreturns to block 604 of FIG. 6.

Additionally or alternatively, the symptom processor 224 of FIG. 2 caninterface with the machine 108 (e.g., directly using the deviceinterface 212 and/or indirectly via the technician device 108) to obtainoutput data of the machine 104 (e.g., photos taken by the machine 104).In such examples, the symptom processor 224 can process an imageobtained by the imaging device to identify an artifact (e.g., flaw)found on the image. The symptom processor 224 can uses identified flawsto identify a problem, a source of the problem, and/or additionalsymptoms. For example, the symptom processor 224 can, based on anidentified flaw, determine that that there is not enough radiation, toomuch radiation, identify a failing detector, misalignment issues, etc.In some examples, the symptom processor 224 can compare the identifiedproblem, source of problem, and/or symptoms associated with the flawwith previous problem-solution information (e.g., stored in the exampleproblem solution database 220), to determine if the problem correspondsto a configuration and/or design flaw. In such examples, the symptomprocessor 224 may transmit an alert to the machine 108, a user of themachine 108 (e.g., via text, email, etc.), a system administrator, etc.identify the design flaw. Additionally, the machine identifier 240 mayupdate fleet information and/or generate an alert to similar machines toidentify the design flaw.

FIG. 8 includes a flowchart 604 representative of machine readableinstructions that can be implemented by the structural components of themachine recovery system 102 of FIG. 2 to perform a smart care packageprocess. Although the flowchart 604 of FIG. 8 is described inconjunction with the machine recovery system 102 of FIGS. 1 and/or 2,other type(s) of recovery system(s) and/or other type(s) of processor(s)can be utilized instead.

At block 800, the care package generator 225 obtains error code(s)(e.g., from the machine log(s) database 218 via the database interface214) and/or indemnified issues from the symptom processor 224. In someexamples, the machine log(s) 218 can include machine identifiers and/orcontextual information that can be used to perform a smart find process.At block 802, the machine identifier 240 determines whether a smart findshould be performed. For example, the machine identifier 240 can performa smart process when the smart find process has yet to be performed andthe error code(s) provide additional information (e.g., machineidentifiers and/or particular contextual information) for the smart findprocess to be performed.

When the machine identifier 240 determines that the smart find is not tobe performed (block 802: NO), then the process continues to block 806.When the machine identifier 240 determines that the smart find processshould be performed (block 802: YES), then the machine recovery system102 performs the smart find process (block 804), as further describedbelow in conjunction with FIG. 10. At block 806, the care packagegenerator 228 generates a care package based on a template layout. Theinitial template layout can be based on technician and/or manufacturerpreferences and can be customizable. At block 808, the care packagegenerator 228 accesses information from the databases 216, 220, 222 viathe database interface 214. For example, the care package generator 228can access (A) contextual information (e.g., customer site and layoutinformation, contract warranty, asset location, historical machineinformation, etc.) from the enterprise system(s) database 216 when themachine 104 has been identified, (B) problem solution data correspondingto the error code(s) and/or identified issues from the problem solutiondatabase 220, and/or (C) system health information corresponding to themachine 104 from the dynamic system health database 222.

At block 810, the care package generator 204 determines whether amodel/digital twin corresponding to the machine 104 is available. Forexample, when the smart find process has been performed, then amodel/digital twin corresponding to the machine 104 has already beenidentified. Additionally or alternatively, a model/digital twin can beselected based on the error code(s), identified issues, and/or accessedinformation. When the care package generator 204 determines that amodel/digital twin corresponding to the machine 104 is not available(block 810: NO), then the process continues to block 814. When the carepackage generator 204 determines that a model/digital twin correspondingto the machine 104 is available (block 810: YES), then the solutionpredictor 230 tests the corresponding model(s)/digital twin(s) based onerror code(s), identified issue(s), and/or accessed information toidentify potential solutions to the problem(s) of the machine.

For example, a digital twin of an x-ray imaging device and/or of acomponent of the x-ray imaging device (e.g., a digital twin of an x-raydetector, etc.) can be processed based on symptom(s) and/or otherinformation. The digital twin models the symptom(s) to determineassociated system issue(s) and/or models the issue(s) to simulate one ormore solutions and associated outcome(s) to help determine a solution or“fix” to be applied to ameliorate the symptom(s) exhibited by themachine 104, for example. Since the digital twin is a physics-basedmodel or replica of the actual machine 104 and its environment,reproduced in virtual space, an impact on the digital twin shouldindicate a similar impact on the actual machine 104.

Alternatively or in addition, a deep learning network, such as the deeplearning network 500, can be applied to take as inputs the symptom(s),issue(s), configuration information for the machine 104, etc., andcorrelate those inputs with resource constraints, customer requirements,machine limitations, etc., to determine a proposed solution to addressthe issue(s), for example.

At block 814, the solution predictor 230 predicts solution(s) based onthe testing of the artificial intelligence model(s) (e.g.,model(s)/digital twin(s), neural network(s), etc.) and/or problemsolution data of the problem solution database 220 that corresponds tothe obtained error code(s), identified issue(s), and/or accessedinformation. At block 816, the solution predictor 230 determines whetherat least one solution was able to be predicted. When at least onesolution was not able to be predicted (block 816: NO), then the processcontinues to block 820. When at least one solution was able to bepredicted (block 816: YES), then the care package generator 228 adds thepredicted solution information to the care package (block 818). Forexample, the care package generator 228 can add parts ofmanual(s)/tutorial(s) corresponding to the predicted solution.

At block 820, the care package generator 228 determines whether thereare tools corresponding to the error code(s), issue(s), and/or predictedsolution(s) that are specialized tools. For example, every techniciancan carry or be required to carry certain tools and can be permitted tonot carry specialized tools that are either too large to carry aroundall the time, are rarely utilized, and/or are too expensive for acompany to purchase a specialized tool for each technician. Accordingly,some specialized tools can be located in a predetermined location wherea technician can obtain the specialized tool when needed. Accordingly,it is preferable to know whether a service request is going to need aspecialized tool, so that the technician can arrive with the tool, asopposed to finding out that he/she needs the tool after already arrivingat the service location. The correspondence between a specialized tooland an error code, issue, and/or predicted solution can be stored in theproblem solution database 220.

If the care package generator 228 determines that one or more tool(s)corresponding to the error code(s), issue(s), and/or predicatedsolution(s) is not a specialized tool (block 820: NO), the processcontinues to block 824. When the care package generator 228 determinesthat one or more tool(s) corresponding to the error code(s), issue(s),and/or predicated solution(s) is a specialized tool (block 820: YES),the care package generator 228 adds an identifier of the specializedtool(s) to the care package (block 822). Thus, the technician is madeaware that he/she can need to obtain the specialized tool for theservice request. At block 824, the care package generator 228 adds otherrelevant information and/or sections of machine manual(s)/tutorial(s) tothe care package base on the error code(s), issue(s), replacementpart(s) that can be needed, site location information (e.g., map(s)and/or directions to the site location), and/or predicted solution(s).

The final care package is a data structure of relevant customer data,machine configuration information, executable instructions/code, etc.,that can be used by a resource to fix and/or identify a problem of themachine 104. When the problem may be fixed remotely (e.g., automaticallyby transmitting instructions to be executed by the machine 104), thecare package may include software instructions, patches, etc. that maybe transmitted to the machine 104 for repair and/or to prompt additionaldata corresponding to the problem. Additionally, the care package mayinclude software that, when executed by the machine 104 identifieswhether or not the problem and/or other problems have been fixed. Whenthe care package is being transmitted to technician device 108 of atechnician, the care package is a data structure that includes relevantand customized data that a technician may need to avoid unnecessary downtime, expense, etc. For example, the care package allows the technicianto identify replacement parts, specialized tools, etc., that are neededto service the request before reaching the location. Additionally, thecare package has identified relevant sections of service manuals, sothat the technician does not have to spend time and effort finding thecorrect manual information corresponding to a machine and/or problem.Additionally, the care package eliminates a new technician looking upincorrect information when servicing the machine 104. Additionally, whenthe care package includes a digital twin, the technician can avoid doingfurther damage to the machine and/or avoid wasting time on unsuccessfulservice techniques by applying techniques to the digital twin first.

FIG. 9 includes a flowchart 606 representative of machine readableinstructions that can be implemented by the structural components of themachine recovery system 102 of FIG. 2 to perform a smart dispatchprocess. Although the flowchart 606 of FIG. 9 is described inconjunction with the machine recovery system 102 of FIGS. 1 and/or 2,other type(s) of recovery system(s) and/or other type(s) of processor(s)can be utilized instead.

At block 902, the remote instruction executor 232 determines whether oneor more of the identified solution(s) corresponds to (e.g., can be fixedvia) one or more remote solution(s). For example, the problem solutiondatabase 220 can include information relating to previous problems thathave been solved or could be solved with remote instruction(s).Accordingly, the remote instruction executor 232 can access the problemsolution database 220 via the database interface 214 using theidentified solutions to determine whether one or more of the identifiedsolutions can be fixed remotely. When the remote instruction executor232 determines that one or more solutions do not correspond to a remotesolution (block 902: NO), then the process continues to block 908. Whenthe remote instruction executor 232 determines that one or moresolutions correspond to a remote solution (block 902: YES), then theremote instruction executor 232 executes the remote solutions(s) to themachine 104 (e.g., directly) and/or as part of a care package to thetechnician (block 904). For example, the remote instruction executor 232can transmit a software patch, reboot instructions, and/or other remoteinstructions to attempt to solve one or more of the problems affectingthe machine. In another example, the remote instruction executor 232alerts the technician to the potential remote instruction solution viathe care package. In such an example, the technician attempts totransmit instructions to the machine 104 remotely to attempt to solvethe problem.

At block 906, the remote instruction executor 232 determines whether theproblem with the machine 104 has been solved. For example, the remoteinstruction executor 232 can determine that the problem with the machine104 has been solved based on network communications directly with themachine 104 and/or based on a completion trigger and/or any othertrigger from the technician device 108 identifying that the servicerequest is complete. When the remote instruction executor 232 determinesthat the problem has been solved (block 906: YES), then the processreturns to block 608 of FIG. 6. When the remote instruction executor 232determines that the problem has not been solved (block 906: NO), thenthe process continues to block 908.

At block 908, the technician selector 234 determines whether theidentified error code(s) and/or issue(s) correspond to a critical error.Error codes and/or issues can be identified as critical error(s) basedon user/manufacture/customer preferences. For example, when a customerhas two of the same machine, any error code from one machine is notcritical, so long as the other machine is operational. In anotherexample, any error code and/or issue that results in total shut down ofthe machine 104 can correspond to a critical error. When the technicianselector 234 determines that the identified error code(s) and/orissue(s) does not correspond to a critical error (block 908: NO), thenthe process continues to block 912. When the technician selector 234determines that the identified error code(s) and/or issue(s) correspondto a critical error (block 908: YES), then the technician selector 234generates a group of available technicians by filtering outtechnician(s) in the technician database 238 who are not available toservice the critical error within a threshold amount of time (block910). For example, the technician selector 234 can analyze the schedulesof the technicians to determine which technicians are immediatelyavailable (e.g., can service the request within a threshold amount oftime based on their schedule and their distance to the service location,etc.). The distance to the service location can include a first distancefrom the technician's current location to a first location to obtain aspecialized to and/or replacement part and a second distance from thefirst location to the service location.

At block 912, the technician selector 234 checks the clinical workflowcorresponding to the machine. The clinical workflow corresponds toseries of machines that are utilized to accomplish a particular task.Other machines in the clinical workflow can be evaluated to determinewhether additional machines are to be serviced (e.g., are due for acheckup, are part of the issue, etc.) and/or whether the problem stemsfrom another machine, for example. For example, when the output of afirst machine is used in a second machine, where the problem wasidentified, then the problem can be cause be an invalid input. In suchan example, even though the problem was identified in the secondmachine, the actual problem can be from the first machine. Accordingly,it can be desirable to select a technician that can service multiplemachines or multiple technicians to service multiple machines in aclinical workflow to avoid sending out different technicians atdifferent times.

At block 914, the technician selector 234 determines whether additionalmachines at the site location need to be serviced (e.g., other machinesin the building or based on the clinical workflow are due for a checkupor also correspond to errors/issues). When the technician selector 234determines that there are no additional machines at the site locationthat need to be serviced (block 914: NO), then the process continues toblock 918. When the technician selector 234 determines that there areadditional machines at the site location that need to be serviced (block914: YES), then the weight multiple 236 adjusts preset weights of theavailable technicians based on the number of additional machines thatthe technician can service (block 916). For example, when the technicianselector 234 identifies three machines that need to/should be serviced,then a higher weight is applied to technicians that can service allthree and a lower weight is applied to technicians that can only serviceone. When a technician cannot service any of the machines, then a weightof zero is applied, thereby filtering out the technician unless they arethe only technician available.

At block 918, the weight multiplier 236 adjusts the weights of theavailable technicians based on skill level, tools, distance,availability, etc. In some examples, the weight multiplier may utilize aneural network (e.g., the example neural network 500 of FIG. 5) togenerate weights based on inputs including the skill level, tools,distance, etc. of the resources. The number of adjustments and/or thestrength of the weights can be based on user/manufacture/customerpreferences. For example, when time is a high priority for a customer,then distance and availability can receive higher weights than skilllevel. At block 920, the technician selector 234 determines the numberof technicians needed to service the machines(s) at the side location.The number of technicians can be based on a contract with the customer,the availability of technicians, the complexity of the machine issue(s),and/or number of machines that need to be serviced. At block 922, thetechnician selector 234 selects the X highest weighted technicians,where X corresponds to the determined number of block 920. At block 924,the technician selector 234 transmits an indication of the servicerequest with the corresponding care package to the selected techniciansvia the device interface 212.

FIG. 10 includes a flowchart 610, 706, 802 representative of machinereadable instructions that can be implemented by the structuralcomponents of the machine recovery system 102 of FIG. 2 to perform asmart find process. Although the flowchart 610, 706, 802 of FIG. 10 isdescribed in conjunction with the machine recovery system 102 of FIGS. 1and/or 2, other type(s) of recovery system(s) and/or other type(s) ofprocessor(s) can be utilized instead.

At block 1002, the machine identifier 240 determines whether the machineID is available. The machine ID can be provided by the user, themachine, the technician (e.g., when servicing the machine), and/or canbe stored in conjunction with the machine 104 in one or more of thedatabases 216, 218, 220, 222. When the machine identifier 240 determinesthat the machine ID is not available (block 1002: NO), then the machineidentifier 240 uses the machine identifier 240 can use obtainedinformation about the machine 104 and compare the obtained information(e.g., contextual information) to stored information in the enterprisesystem(s) database 216 to identify the machine (block 1004). In someexamples, the obtained information may include one or more photo(s) ofthe machine 104. In such an example, the machine identifier 240 utilizeimage processing techniques to determine distinct features of themachine 104 and/or where the machine 104 is located and compare theprocessed photos and/or distinct features to reference data and/orreference location information to identify the machine 104. In someexamples, the machine identifier 420 may utilize a neural network (e.g.,the example neural network 500 of FIG. 5) to identify the machine basedon contextual information.

If the machine identifier 240 determines that the machine ID isavailable (block 1002: YES), the machine identifier 240 identifies themachine 104 based on the machine ID (block 1006). At block 1008, themachine identifier 240 determines the make, model, modality, and/orother information of the identified machine. For example, the machineidentifier 240 can access the enterprise system(s) database 216 via thedatabase interface 214 to obtain information corresponding to theidentified machine. At block 1010, the model generator 242 determineswhether there is a corresponding fleet to the make, model, modalityand/or other information of the identified machine. The fleets ofmachines have similar makes, models, modalities, and/or otherinformation. Information from a machine in a fleet can be used todiagnosis, predict problems, and/or service other machines in the fleet.Additionally, the fleets correspond to an artificial intelligencemodel(s) (e.g., a model, a digital twin, and/or a neural network) thathas been customized based on information obtained from the fleet. Insome examples, a machine can correspond to multiple fleets. The fleetinformation can be stored in the enterprise system(s) database 216.

If the model generator 242 determines that there is not a correspondingfleet of machines that (block 1010: NO), then the model generator 242selects a template artificial intelligence model (e.g., model/digitaltwin, neural network, etc.) based on the make, modality, and/or model(block 1012). At block 1014, the model generator 242 generates anartificial intelligence model (e.g., model/digital twin, neural network,etc.) for the machine 104 by modifying the template artificialintelligence model (e.g., model/digital twin, neural network, etc.) withmodality and/or other available information corresponding to themachine. When the model generator 242 determines that there is acorresponding fleet of machines that (block 1010: YES), then the modelgenerator 242 selects the artificial intelligence model(s) (e.g.,model(s), digital twin(s), neural network(s), etc.) of the correspondingfleet(s) (block 1016). At block 1018, the model generator 242 deploysthe artificial intelligence model(s) (e.g., model(s), digital twin(s),neural network(s), etc.) of the corresponding fleet(s) to the device 108of the technician (e.g., which can be included in the smart package) viathe device interface 212. The technician can utilize the artificialintelligence model(s) (e.g., model(s), digital twin(s), neuralnetwork(s), etc.) as a test machine prior to/during service of themachine 104.

At block 1020, the information updater 246 stores the error information(e.g., the error codes, identified issues, etc.) of the identifiedmachine in correspondence with the fleet (e.g., in the enterprisesystem(s) database 216 via the database interface 214, etc.). Using theerror information, the fleet can track the error(s)/issue(s) of machineswithin the fleet to develop further analysis and/or to predict futureerrors within the fleet. At block 1022, the information updater 246determines whether the other machines in the fleet correspond to similarerror(s)/issue(s). When the information updater 246 determines that theother machines in the fleet correspond to similar error(s)/issue(s),then the information updater 246 flags the error/issue as a common errorin association with contextual information (e.g., when the erroroccurred, the number of performances before the error occurred, theamount of time between the last servicing and the error, the age of themachine, location information, etc.). At block 1026, the deviceinterface 212 transmits a warning corresponding to the flag for machineswith similar contextual information. For example, when the erroroccurred when the machine 104 was operational for five years, then thedevice interface 212 can transmit a warning to other machines that havebeen operation for more than four years. The device interface 212 cantransmit the warning to the device, to a user of the device (e.g., viaemail, text message, etc.), to the customer, to the manager of themachine recovery system 102, to the service center 106, and/or to anyother related party.

FIG. 11 is a block diagram of a processor platform 1100 structured toexecute the instructions of FIGS. 6, 7, 8, 9, and/or 10 to implement themachine recovery system 102 of FIG. 2. The processor platform 1100 canbe, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), an Internet appliance,or any other type of computing device.

The processor platform 1100 of the illustrated example includes aprocessor 1112. The processor 1112 of the illustrated example ishardware. For example, the processor 1112 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor can be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 1112 implements the device interface 212,the database interface 214, the symptom processor 224, the filter 226,the care package generator 228, the solution predictor 230, the remoteinstruction executor 232, the technician selector 234, the weightmultiplier 236, the technician database 238, the machine identifier 240,the model generator 242, the survey generator 244, the informationupdater 246, and the database interface 214.

The processor 1112 of the illustrated example includes a local memory1113 (e.g., a cache). The processor 1112 of the illustrated example isin communication with a main memory including a volatile memory 1114 anda non-volatile memory 1116 via a bus 1118. The volatile memory 1114 canbe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1116 can be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1114,1116 is controlled by a memory controller.

The processor platform 1100 of the illustrated example also includes aninterface circuit 1120. The interface circuit 1120 can be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1122 are connectedto the interface circuit 1120. The input device(s) 1122 permit(s) a userto enter data and/or commands into the processor 1112. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1124 are also connected to the interfacecircuit 1120 of the illustrated example. The output devices 1124 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1120 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1120 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1126. The communication canbe via, for example, an Ethernet connection, a tech subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1100 of the illustrated example also includes oneor more mass storage devices 1128 for storing software and/or data.Examples of such mass storage devices 1128 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 1132 of FIGS. 6, 7, 8, 9, and/or 10can be stored in the mass storage device 1128, in the volatile memory1114, in the non-volatile memory 1116, and/or on a removablenon-transitory computer readable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that providenew, technologically advanced imaging modality maintenance, monitoring,and repair. The disclosed methods, apparatus and articles of manufactureimprove the efficiency of using a computing device by transforming thecomputing device into a diagnostic and repair tool to receive symptomand/or configuration information (e.g., through data transfer,measurement, monitoring, etc.) for a machine, identify a problem forthat machine and/other connected component(s), model and drive selectionof a solution to the problem, and identify appropriate resource(s) toexecute the solution to ameliorate the symptom(s)/problem(s) at amachine. Certain examples provide simulation through modeling and/ornetwork analysis and generate a care package to be deployed to fix aproblem. The disclosed methods, apparatus and articles of manufactureare accordingly directed to one or more improvement(s) in thefunctioning of a computer.

For example, outcomes and observed behavior can be modeled to developrealistic, physics-based virtual counterparts to imaging modalitydevices, repair resources, environments, etc., and/or robust learningnetwork models to generate solutions from indications of a problem for amachine. Certain examples factor in a severity of a problem, animmediacy of a problem, a scope of a problem, as well as availabletools, technicians, and/or other resources to generate a packagedsolution to address the issue for the target machine and/or othermachines in its fleet, other devices in its environment, othercomponents on its care path/protocol, etc. Rather than sendinginadequate resources enable to solve an issue, spend hundreds of dollarsa day on travel and time, allow issues to intensify and cost more tofix, resulting in greater machine unavailability, certain examplesgenerate a repair or care package to diagnose and/or treat issue(s) withmachine(s). Solutions can be tailored to a particular machine in aparticular environment and/or can extend to similar machines at alocation, in a fleet, etc. While prior approaches involved manualtechnician investigation, certain examples provide customized, evolvingsolutions for particular machine(s) with particular problem(s). Forexample, an MR machine used for 12 scan a day can be modeled and treateddifferently than another MR machine used for 30 scans a day. Machinescan be compared to allocate a same response and/or deviate to develop adifferent response for a machine whose usage pattern is different fromothers in the fleet. Certain examples provide fleet modeling, sitemodeling, workflow/protocol modeling, etc., to leverage environment forthe benefit of machine repair and leverage one machine's repair for thebenefit of the larger environment.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A non-transitory computer readable storage mediumcomprising instructions which, when executed, cause a machine to atleast: model a problem using an artificial intelligence model of animaging device based on information obtained from a service request fromthe imaging device; identify at least one of a skill level, tools list,or replacement part list to fix the problem based on the modeling usingthe artificial intelligence model; access resources from a database ofresources available to service the imaging device to identify a set ofone or more available resources; in response to the service request forthe imaging device corresponding to an issue, determine whether theissue of the imaging device corresponds to a critical error; when theissue of the imaging device corresponds to the critical error, filterout resources that are not available to service the imaging devicewithin a threshold amount of time to reduce the set of one or moreavailable resources; weight resources in the set of one or moreavailable resources based on at least one of the skill level, possessedtools in comparison to the tools list, possessed replacement parts incomparison to the replacement part list, distance to service location,or availability to identify a highest weighted resource; and transmitthe service request using a wireless communication to a repair deviceassociated with the highest weighed resource.
 2. The computer readablestorage medium of claim 1, wherein the critical error is defined by atleast one of a manufacturer of the imaging device or a customer.
 3. Thecomputer readable storage medium of claim 1, wherein the instructionscause the machine to determine if the resource is available to servicethe imaging device based on at least one of an availability of theresource or a distance of the resource to the service location.
 4. Thecomputer readable storage medium of claim 3, wherein the distance of theresource to the service location includes a first distance from a firstlocation of the resource to a second location of at least one of a toolor a replacement needed for the service request and a second distancefrom the second location to the service location.
 5. The computerreadable storage medium of claim 1, wherein the instructions cause themachine to determine a clinical workflow corresponding to the imagingdevice.
 6. The computer readable storage medium of claim 5, wherein theinstructions cause the machine to identify additional imaging devicesfor servicing based on the clinical workflow.
 7. The computer readablestorage medium of claim 6, wherein the instructions cause the machine toweight the resources based on a number of the additional imaging devicesthat a technician is capable of servicing.
 8. The computer readablestorage medium of claim 1, wherein the artificial intelligence model isat least one of a digital twin of the imaging device or a neuralnetwork.
 9. The computer readable storage medium of claim 1, wherein theservice request is included in a care package.
 10. An apparatuscomprising: a technician selector to: identify at least one of a skilllevel, tools list, or replacement part list to fix a problem based on anidentified problem corresponding to a service request from an imagingdevice; and access resources from a database of resources available toservice the imaging device; a multiplier to weight resources based on atleast one of the skill level, possessed tools in comparison to the toolslist, possessed replacement parts in comparison to the replacement partlist, distance to a location of the imaging device, or availability; andan interface to transmit the service request using a wirelesscommunication to a repair device of the highest weighed resource, theservice request to be augmented to include a configuration for therepair device to facilitate addressing of the service request by thehighest weighted resource.
 11. The apparatus of claim 10, wherein thetechnician selector is to, when the identified problem corresponds to acritical error, remove resources that are not available within athreshold amount of time, the critical error defined by at least one ofa manufacturer of the imaging device or a customer.
 12. The apparatus ofclaim 10, wherein the technician selector is to determine if theresource is available to service the imaging device based on at leastone of an availability of the resource or a distance of the resource tothe location of the imaging device.
 13. The apparatus of claim 12,wherein the distance of the resource to the location of the imagingdevice includes a first distance from a first location of the resourceto a second location of at least one of a tool or a replacement neededfor the service request and a second distance from the second locationto the location of the imaging device.
 14. The apparatus of claim 10,wherein the technician selector is to determine a clinical workflowcorresponding to the imaging device.
 15. The apparatus of claim 14,wherein the technician selector is to identify additional imagingdevices for servicing based on the clinical workflow.
 16. The apparatusof claim 15, wherein the multiplier is to weight the resources based ona number of the additional imaging devices that a technician is capableof servicing.
 17. The apparatus of claim 10, wherein the service requestis included in a care package.
 18. A method comprising: identifying, byexecuting an instruction with a processor, at least one of a skilllevel, tools list, or replacement part list to fix a problem of amachine based on a digital twin of the machine; accessing resources froma database of resources available to service an imaging device;determining, by executing an instruction with the processor, whether theproblem of the imaging device corresponds to a critical error; when theproblem of the imaging device corresponds to the critical error,removing, by executing an instruction with the processor, resources whoare not available to service the imaging device within a thresholdamount of time; weighting, by executing an instruction with theprocessor, resources based on at least one of the skill level, possessedtools in comparison to the tools list, possessed replacement parts incomparison to the replacement part list, distance to service location,or availability to determine a highest weighted resource; andtransmitting a service request corresponding to the imaging device to arepair device of the highest weighed resource using a wirelesscommunication.
 19. The method of claim 18, wherein the critical errordefined by at least one of a manufacturer of the imaging device or acustomer.
 20. The method of claim 18, further including determining if atechnician is available to service the imaging device based on at leastone of an availability of the resource or a distance of the resource tothe location of the imaging device.